class_docstr: This class represents the internal state of individual tracked objects observed as bbox.
  
  Args:
      bbox (np.array): bbox in [x1,y1,x2,y2,score] format.
      delta_t (int): delta_t of previous observation
class_docstr_tok: ["This","class","represents","the","internal","state","of","individual","tracked","objects","observed","as","bbox",".","Args",":","bbox","(","np.array",")",":","bbox","in","[","x1",",","y1",",","x2",",","y2",",","score","]","format",".","delta_t","(","int",")",":","delta_t","of","previous","observation"]
code: def __init__(self, bbox, delta_t=3):
      self.kf = OCSORTKalmanFilter(dim_x=7, dim_z=4)
      self.kf.F = np.array(
          [
              [1.0, 0, 0, 0, 1.0, 0, 0],
              [0, 1.0, 0, 0, 0, 1.0, 0],
              [0, 0, 1.0, 0, 0, 0, 1.0],
              [0, 0, 0, 1.0, 0, 0, 0],
              [0, 0, 0, 0, 1.0, 0, 0],
              [0, 0, 0, 0, 0, 1.0, 0],
              [0, 0, 0, 0, 0, 0, 1.0],
          ]
      )
      self.kf.H = np.array(
          [
              [1.0, 0, 0, 0, 0, 0, 0],
              [0, 1.0, 0, 0, 0, 0, 0],
              [0, 0, 1.0, 0, 0, 0, 0],
              [0, 0, 0, 1.0, 0, 0, 0],
          ]
      )
      self.kf.R[2:, 2:] *= 10.0
      self.kf.P[4:, 4:] *= 1000.0
      # give high uncertainty to the unobservable initial velocities
      self.kf.P *= 10.0
      self.kf.Q[-1, -1] *= 0.01
      self.kf.Q[4:, 4:] *= 0.01
  
      self.score = bbox[4]
      self.kf.x[:4] = convert_bbox_to_z(bbox)
      self.time_since_update = 0
      self.id = KalmanBoxTracker.count
      KalmanBoxTracker.count += 1
      self.history = []
      self.hits = 0
      self.hit_streak = 0
      self.age = 0
      """
          NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of 
          function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a 
          fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.
          """
      self.last_observation = np.array(
          [-1, -1, -1, -1, -1]
      )  # placeholder
      self.observations = dict()
      self.history_observations = []
      self.velocity = None
      self.delta_t = delta_t
  
file_path: PaddleDetection/deploy/pptracking/python/mot/tracker/ocsort_tracker.py
imports: import time
  import numpy
  from ..matching.ocsort_matching import associate, linear_assignment, iou_batch, associate_only_iou
  from ..motion.ocsort_kalman_filter import OCSORTKalmanFilter
label_desc: Use image node_id 1 to create a new KalmanBoxTracker object from inherited base classes: object with example: obj = KalmanBoxTracker(bbox, delta_t)